Deleted vendor folder

1 files changed, 0 insertions, 146 deletions

diff --git a/vendor/adler/src/algo.rs b/vendor/adler/src/algo.rs
deleted file mode 100644
index 650cffa..0000000
--- a/vendor/adler/src/algo.rs
+++ /dev/null
@@ -1,146 +0,0 @@
-use crate::Adler32;
-use std::ops::{AddAssign, MulAssign, RemAssign};
-
-impl Adler32 {
-    pub(crate) fn compute(&mut self, bytes: &[u8]) {
-        // The basic algorithm is, for every byte:
-        //   a = (a + byte) % MOD
-        //   b = (b + a) % MOD
-        // where MOD = 65521.
-        //
-        // For efficiency, we can defer the `% MOD` operations as long as neither a nor b overflows:
-        // - Between calls to `write`, we ensure that a and b are always in range 0..MOD.
-        // - We use 32-bit arithmetic in this function.
-        // - Therefore, a and b must not increase by more than 2^32-MOD without performing a `% MOD`
-        //   operation.
-        //
-        // According to Wikipedia, b is calculated as follows for non-incremental checksumming:
-        //   b = n×D1 + (n−1)×D2 + (n−2)×D3 + ... + Dn + n*1 (mod 65521)
-        // Where n is the number of bytes and Di is the i-th Byte. We need to change this to account
-        // for the previous values of a and b, as well as treat every input Byte as being 255:
-        //   b_inc = n×255 + (n-1)×255 + ... + 255 + n*65520
-        // Or in other words:
-        //   b_inc = n*65520 + n(n+1)/2*255
-        // The max chunk size is thus the largest value of n so that b_inc <= 2^32-65521.
-        //   2^32-65521 = n*65520 + n(n+1)/2*255
-        // Plugging this into an equation solver since I can't math gives n = 5552.18..., so 5552.
-        //
-        // On top of the optimization outlined above, the algorithm can also be parallelized with a
-        // bit more work:
-        //
-        // Note that b is a linear combination of a vector of input bytes (D1, ..., Dn).
-        //
-        // If we fix some value k<N and rewrite indices 1, ..., N as
-        //
-        //   1_1, 1_2, ..., 1_k, 2_1, ..., 2_k, ..., (N/k)_k,
-        //
-        // then we can express a and b in terms of sums of smaller sequences kb and ka:
-        //
-        //   ka(j) := D1_j + D2_j + ... + D(N/k)_j where j <= k
-        //   kb(j) := (N/k)*D1_j + (N/k-1)*D2_j + ... + D(N/k)_j where j <= k
-        //
-        //  a = ka(1) + ka(2) + ... + ka(k) + 1
-        //  b = k*(kb(1) + kb(2) + ... + kb(k)) - 1*ka(2) - ...  - (k-1)*ka(k) + N
-        //
-        // We use this insight to unroll the main loop and process k=4 bytes at a time.
-        // The resulting code is highly amenable to SIMD acceleration, although the immediate speedups
-        // stem from increased pipeline parallelism rather than auto-vectorization.
-        //
-        // This technique is described in-depth (here:)[https://software.intel.com/content/www/us/\
-        // en/develop/articles/fast-computation-of-fletcher-checksums.html]
-
-        const MOD: u32 = 65521;
-        const CHUNK_SIZE: usize = 5552 * 4;
-
-        let mut a = u32::from(self.a);
-        let mut b = u32::from(self.b);
-        let mut a_vec = U32X4([0; 4]);
-        let mut b_vec = a_vec;
-
-        let (bytes, remainder) = bytes.split_at(bytes.len() - bytes.len() % 4);
-
-        // iterate over 4 bytes at a time
-        let chunk_iter = bytes.chunks_exact(CHUNK_SIZE);
-        let remainder_chunk = chunk_iter.remainder();
-        for chunk in chunk_iter {
-            for byte_vec in chunk.chunks_exact(4) {
-                let val = U32X4::from(byte_vec);
-                a_vec += val;
-                b_vec += a_vec;
-            }
-            b += CHUNK_SIZE as u32 * a;
-            a_vec %= MOD;
-            b_vec %= MOD;
-            b %= MOD;
-        }
-        // special-case the final chunk because it may be shorter than the rest
-        for byte_vec in remainder_chunk.chunks_exact(4) {
-            let val = U32X4::from(byte_vec);
-            a_vec += val;
-            b_vec += a_vec;
-        }
-        b += remainder_chunk.len() as u32 * a;
-        a_vec %= MOD;
-        b_vec %= MOD;
-        b %= MOD;
-
-        // combine the sub-sum results into the main sum
-        b_vec *= 4;
-        b_vec.0[1] += MOD - a_vec.0[1];
-        b_vec.0[2] += (MOD - a_vec.0[2]) * 2;
-        b_vec.0[3] += (MOD - a_vec.0[3]) * 3;
-        for &av in a_vec.0.iter() {
-            a += av;
-        }
-        for &bv in b_vec.0.iter() {
-            b += bv;
-        }
-
-        // iterate over the remaining few bytes in serial
-        for &byte in remainder.iter() {
-            a += u32::from(byte);
-            b += a;
-        }
-
-        self.a = (a % MOD) as u16;
-        self.b = (b % MOD) as u16;
-    }
-}
-
-#[derive(Copy, Clone)]
-struct U32X4([u32; 4]);
-
-impl U32X4 {
-    fn from(bytes: &[u8]) -> Self {
-        U32X4([
-            u32::from(bytes[0]),
-            u32::from(bytes[1]),
-            u32::from(bytes[2]),
-            u32::from(bytes[3]),
-        ])
-    }
-}
-
-impl AddAssign<Self> for U32X4 {
-    fn add_assign(&mut self, other: Self) {
-        for (s, o) in self.0.iter_mut().zip(other.0.iter()) {
-            *s += o;
-        }
-    }
-}
-
-impl RemAssign<u32> for U32X4 {
-    fn rem_assign(&mut self, quotient: u32) {
-        for s in self.0.iter_mut() {
-            *s %= quotient;
-        }
-    }
-}
-
-impl MulAssign<u32> for U32X4 {
-    fn mul_assign(&mut self, rhs: u32) {
-        for s in self.0.iter_mut() {
-            *s *= rhs;
-        }
-    }
-}